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210123 ||| eng |
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|a 9781838988654
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| 050 |
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|a QA76.73.P98
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1 |
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|a Ciaburro, Giuseppe
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|a Hands-on simulation modeling with Python
|b develop simulation models to get accurate results and enhance decision-making processes
|c Giuseppe Ciaburro
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| 260 |
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|a Birmingham, UK
|b Packt Publishing
|c 2020
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| 300 |
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|a 1 volume
|b illustrations
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| 505 |
0 |
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|a Mean and variance -- Uniform distribution -- Binomial distribution -- Normal distribution -- Summary -- Section 2: Simulation Modeling Algorithms and Techniques -- Chapter 4: Exploring Monte Carlo Simulations -- Technical requirements -- Introducing Monte Carlo simulation -- Monte Carlo components -- First Monte Carlo application -- Monte Carlo applications -- Applying the Monte Carlo method for Pi estimation -- Understanding the central limit theorem -- Law of large numbers -- Central limit theorem -- Applying Monte Carlo simulation -- Generating probability distributions
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|a The random.random() function -- The random.seed() function -- The random.uniform() function -- The random.randint() function -- The random.choice() function -- The random.sample() function -- Generating real-valued distributions -- Summary -- Chapter 3: Probability and Data Generation Processes -- Technical requirements -- Explaining probability concepts -- Types of events -- Calculating probability -- Probability definition with an example -- Understanding Bayes' theorem -- Compound probability -- Bayes' theorem -- Exploring probability distributions -- Probability density function
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| 505 |
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|a Random number simulation -- Probability distribution -- Properties of random numbers -- The pseudorandom number generator -- The pros and cons of a random number generator -- Random number generation algorithms -- Linear congruential generator -- Random numbers with uniform distribution -- Lagged Fibonacci generator -- Testing uniform distribution -- The chi-squared test -- Uniformity test -- Exploring generic methods for random distributions -- The inverse transform sampling method -- The acceptance-rejection method -- Random number generation using Python -- Introducing the random module
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|a Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Getting Started with Numerical Simulation -- Chapter 1: Introducing Simulation Models -- Introducing simulation models -- Decision-making workflow -- Comparing modeling and simulation -- Pros and cons of simulation modeling -- Simulation modeling terminology -- Classifying simulation models -- Comparing static and dynamic models -- Comparing deterministic and stochastic models -- Comparing continuous and discrete models -- Approaching a simulation-based problem
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|a Problem analysis -- Data collection -- Setting up the simulation model -- Simulation software selection -- Verification of the software solution -- Validation of the simulation model -- Simulation and analysis of results -- Dynamical systems modeling -- Managing workshop machinery -- Simple harmonic oscillator -- Predator-prey model -- Summary -- Chapter 2: Understanding Randomness and Random Numbers -- Technical requirements -- Stochastic processes -- Types of stochastic process -- Examples of stochastic processes -- The Bernoulli process -- Random walk -- The Poisson process
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|a Includes bibliographical references
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| 653 |
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|a Simulation methods / fast
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|a Simulation methods / http://id.loc.gov/authorities/subjects/sh85122767
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| 653 |
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|a Computer programming / fast
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| 653 |
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|a Computer simulation / http://id.loc.gov/authorities/subjects/sh85029533
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Computer simulation / fast
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Computer Simulation
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| 653 |
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|a simulation / aat
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| 653 |
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|a simulation methods / aat
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| 653 |
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|a Méthodes de simulation
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| 653 |
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|a Decision making / Data processing
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| 653 |
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|a Prise de décision / Informatique
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| 653 |
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|a Decision making / Data processing / fast
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Simulation par ordinateur
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9781838985097
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781838985097/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 153.83
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|a 003.3
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| 520 |
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|a Developers working with the simulation models will be able to put their knowledge to work with this practical guide. You will work with real-world data to uncover various patterns used in complex systems using Python. The book provides a hands-on approach to implementation and associated methodologies to improve or optimize systems
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